Build, evaluate, and explain your first predictive models.
Learn supervised learning, model evaluation, feature thinking, and practical ML workflows with Python. Learn through live guidance, practical code labs, instructor feedback, and an industry-tested curriculum.
This course focuses on practical application, guided work, and subject-specific projects.
Learning Outcomes
Frame prediction problems
Prepare features and targets
Split data correctly
Train regression models
Build classification models
Evaluate model performance
Compare competing models
Explain predictions responsibly
Practical Skills
Project-Based Learning
Every major skill is connected to realistic practice. Expand a project to see the subject-specific workflow, sample output, and deliverable.
Predict future sales from realistic business features.
Output: Regression notebook + forecast
Model Scorecard
Predict which customers are at higher risk of leaving.
Output: Churn model + retention insight
Model Scorecard
Estimate which customers may respond to a campaign.
Output: Response model + targeting list
Model Scorecard
Compare models using appropriate metrics and validation.
Output: Model scorecard + selection
Model Scorecard
Investigate which features influence predictions most.
Output: Feature report + chart
Model Scorecard
Course output 01
Course output 02
Course output 03
Course output 04
Course output 05
Build, evaluate, and explain a predictive solution from a raw dataset.
Course Tools
Model development.
Data preparation.
ML algorithms.
Experiment notebooks.
Work with subject-appropriate business scenarios.
Create outputs yourself with instructor guidance.
Connect technical work to a useful conclusion.
Finish with work you can demonstrate.
Next Live Cohort
Join the next live cohort, practice with realistic work, and complete a course-specific capstone.
Prerequisites
A laptop for in-class practice and project coding labs.
Basic computer confidence with files, web tools, and spreadsheets.
Commitment to attend live interactive sessions and complete homework.
No advanced mathematics or coding required for foundation tracks.
Target Audience
Cohort Logistics
Project-based labs
8 weeks
Khmer and English
Contact an advisor on Telegram to confirm specific cohort start dates, schedule choices, and seat availability.
Faculty
Courses are prepared with bilingual Khmer and English explanations, guided code examples, and supportive feedback.
Certification
Credentials reflect live attendance, code assignments, project capstone submission, and instructor rubric review.
Live program attendance
Weekly assignment labs
Final project capstone
Instructor rubric evaluation
Enrollment Process
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Curriculum Structure
Interactive live sessions where concepts are coded live and reviewed with students.
Build demonstrable outputs: notebooks, dashboards, SQL warehouses, and ML models.
24/7 access to cohort recordings, datasets, assignments, and rubric grading.
Course Questions
No. Data Insight Cambodia programs are live instructor-led cohorts. Recordings and portal resources are provided to help students review between sessions.
Beginner programs start from scratch. Intermediate programs outline their recommended preparation during the 1-on-1 advisor consultation.
Students receive an official verifiable digital credential after completing the required live sessions, assignments, and final capstone review.
We support students with practical capstones, portfolio reviews, career consultation, and partner introductions.
Admissions Desk
Connect with Data Insight Cambodia on Telegram to verify schedule options, curriculum questions, and enrollment details.